Leverage AI-assisted development tools (e.g., Claude Code, MCP servers, Gemini…) to boost development speed, enhance code quality, and accelerate prototyping.
Review AI-generated code for security and architectural fit, while driving safe AI workflows across the team.
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Academic Excellence: Proven track record of publications in international AI/CS conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, KDD, SIGIR, WWW) or top rankings in recognized algorithmic competitions.
Domain-Specific Impact: Hands-on experience in leading or participating in key projects related to Search, Advertising, Recommendation systems, or Large Language Models (LLMs).
Advanced Multimodal Applications: Specialized expertise in multimodal large models, particularly in long-text processing or applications within the film and television drama domains.
Work on hands-on experiments for few of the following tasks:1. Design and Fabrication of Wearable Sensors2. Design, modelling and testing of Wearable Sensors aiming for human-machine-interface3.Write 1 or more research papers based on derived data of the above devices in the first year in the top-notch journals
Have learned wearable sensors related courses for one semester at post-graduate level and earned score of A- or better.2. Strong background in hands-on microfabrication techniques at clean room and with journal publications showing such evidences3. With 1 year and more experience in making and testing wearable sensors4. Fluent in English speaking and writing5. Having more than 2 1st-authored international peer-reviewed journal papers, e.g., Nature Communications and Science Advances (for Research Fellow)
To support the UrbanHeatMap project, which aims to develop scalable, high-resolution ambient temperature mapping methods by integrating outdoor microclimate measurements, mobile sensing data, satellite-derived land surface temperature, and urban morphology information. The Research Assistant will work closely with Research Fellows and the Principal Investigator in generating high-quality field validation datasets and supporting data-driven urban heat research across selected test-bed sites in Singapore. This role is suitable for candidates interested in urban climate, environmental sensing, outdoor thermal comfort, and data-driven approaches to urban heat mitigation.
Urban Microclimate Measurement and Field Campaigns: · Contribute to the planning and execution of outdoor microclimate measurement campaigns across selected HDB precincts and test-bed sites, including NUS campus and SIT Punggol Digital District. · Support the development and implementation of field measurement protocols to ensure data consistency, repeatability, and scientific validity across different sites and measurement periods. · Ensure that field measurements are properly synchronised with project requirements, including model validation, sensor comparison, and site-level urban heat assessment.
Microclimate Data Processing and Quality Assurance: · Process and organise datasets collected from stationary, mobile weather stations, drone-related measurements, and other field sensing platforms. · Conduct data quality assurance and quality control. · Prepare clean, georeferenced, and well-documented datasets for modelling, validation, and analysis by linking sensor measurements with GPS coordinates, site information, and urban morphology attributes. · Support analysis and visualisation of microclimate patterns, including temporal trends, spatial variation, site comparisons, and summary statistics.
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PhD in Computer Science, Artificial Intelligence, Machine Learning, or related fields preferred or strong industry experience in similar fields, ideally with familiarity with Asia-Pacific/Singapore research resources.
Proficient in deep learning; candidates with hands-on experience in training and inference of large-scale models, such as LLMs, VLMs, VLAs, and World Models, are preferred.
Strong programming skills, proficient in C++ and Python; Candidates with awards in competitive programming contests (e.g., ACM/NOI/IOI) are a plus.
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PhD in Computer Science, Artificial Intelligence, Machine Learning, or related fields preferred or strong industry experience in similar fields, ideally with familiarity with Asia-Pacific/Singapore research resources.
Proficient in deep learning; candidates with hands-on experience in training and inference of large-scale models, such as LLMs, VLMs, VLAs, and World Models, are preferred.
Strong programming skills, proficient in C++ and Python; Candidates with awards in competitive programming contests (e.g., ACM/NOI/IOI) are a plus.
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Translate complex engineering trade-offs into CFO- and CIO-ready business cases—covering revenue impact, cost reduction, and risk mitigation.
Guarantee all deployments meet SOC 2, ISO 27001, and regional regulatory standards including China PIPL, Hong Kong PDPO, and financial services sector requirements.
Navigate cross-border data governance with rigor—D&B’s credibility depends on it.
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